[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5089":3,"related-5089":63},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":38,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":62},5089,"Integrating Remote Sensing, Multi-Objective Optimization, and Agent-Based Modelling for Nature-Based Flood Mitigation","https:\u002F\u002Fdoi.org\u002F10.66838\u002Fsauc.6441","By 2030, pluvial flooding is estimated to cause nearly $500 billion in global damage, affecting 54 million people (UNDRR, 2015) each year. By 2050, metropolitan assets worth around $158 trillion could be at risk of floods. This rising risk has sparked an increased interest in nature-based solutions (NbS), as traditional hydraulic infrastructure may lack the flexibility required to manage more complex flood hazards. This study looks into the use of a data-driven approach to identify and assess the effectiveness of upstream afforestation in preventing pluvial floods. We constructed a thorough computational framework for the Girona\u002FEmpordà region of Catalonia, Spain. This framework combines cloud-based remote sensing via Google Earth Engine, multi-criteria decision analysis (MCDA), multi-objective evolutionary optimisation via NSGA-II, and agent-based flood simulation, which is linked to a reinforcement learning agent based on Proximal Policy Optimisation. The framework functions on two stacked spatial scales. At a regional scale (20 x 20 km), it finds and selects suitable afforestation patches. At a meso scale (1 x 1 km), it simulates flood responses to various afforestation schemes. The findings illustrate the technological viability of combining different methods into a single computing process. Preliminary reinforcement-learning results indicate a directional improvement in simulated flood outcomes.","预计到2030年，由暴雨引发的洪水将在全球造成近5000亿美元的损失，每年影响5400万人（UNDRR，2015）。到2050年，价值约158万亿美元的城市资产可能面临洪水风险。这一不断上升的风险引发了人们对基于自然的解决方案（NbS）日益浓厚的兴趣，因为传统的水利基础设施可能缺乏应对更复杂洪水灾害所需的灵活性。本研究探讨了采用数据驱动方法来识别和评估上游造林在预防暴雨洪水方面的有效性。我们为西班牙加泰罗尼亚的赫罗纳\u002F恩波尔达地区构建了一个全面的计算框架。该框架整合了通过Google Earth Engine进行的云端遥感、多准则决策分析（MCDA）、通过NSGA-II进行的多目标进化优化，以及基于智能体的洪水模拟，并与基于近端策略优化（Proximal Policy Optimisation）的强化学习智能体相连接。该框架在两个叠加的空间尺度上运行。在区域尺度（20×20公里）上，它寻找并筛选适宜的造林地块。在中观尺度（1×1公里）上，它模拟不同造林方案下的洪水响应。研究结果证明了将不同方法整合为单一计算流程的技术可行性。初步的强化学习结果表明，模拟洪水结果呈现出方向性改善。",null,"Street Art & Urban Creativity","2026-10-15T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦城市雨洪与自然解决方案，与三农、农业信息化、智慧农业等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":37},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":36},"I3",14,true,[33],"remote sensing",[35,33],"遥感",[],"农业相关性需要复核",[],"IntegratingRemoteSensing,Multi-Objective-5089",1,"10.66838\u002Fsauc.6441",{"doi":41,"openalex_id":43,"authors":44,"venue":10,"cited_by_count":15,"oa_url":53,"card":54,"direction":60,"ingested_from":61},"W7222161377",[45,47,49,51],{"name":46,"orcid":9},"Yashashvi Agrawal",{"name":48,"orcid":9},"Marcos Navarrete Pardo",{"name":50,"orcid":9},"Eleni Ntagkouli",{"name":52,"orcid":9},"Samarth Pachchigar","https:\u002F\u002Fvisualcompublications.es\u002FSAUC\u002Farticle\u002Fdownload\u002F6441\u002F4870",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"集成遥感、多目标优化与智能体模拟，评估上游造林对雨洪的缓解效果。","Google Earth Engine遥感、MCDA、NSGA-II、智能体洪水","多方法融合技术可行，强化学习初步显示模拟洪水结果有方向性改善。","农业绿色发展与碳","可探索将NbS洪水缓解框架扩展至农业流域，耦合碳汇与作物产量多目标优化。","农业遥感与作物表型","openalex","2026-10-16T23:30:27.006605Z",{"total":15,"page":40,"page_size":40,"items":64},[]]